Li Qing, Li Saize, Li Runrui, Wu Wei, Dong Yunyun, Zhao Juanjuan, Qiang Yan, Aftab Rukhma
College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.
Department of Clinical Laboratory, Affiliated People's Hospital of Shanxi Medical University, Shanxi Provincial People's Hospital, Taiyuan, China.
Quant Imaging Med Surg. 2022 Mar;12(3):1929-1957. doi: 10.21037/qims-21-465.
Computed tomography (CT) is widely used in medical diagnoses due to its ability to non-invasively detect the internal structures of the human body. However, CT scans with normal radiation doses can cause irreversible damage to patients. The radiation exposure is reduced with low-dose CT (LDCT), although considerable speckle noise and streak artifacts in CT images and even structural deformation may result, significantly undermining its diagnostic capability.
This paper proposes a multistage network framework which gradually divides the entire process into 2-staged sub-networks to complete the task of image reconstruction. Specifically, a dilated residual convolutional neural network (DRCNN) was used to denoise the LDCT image. Then, the learned context information was combined with the channel attention subnet, which retains local information, to preserve the structural details and features of the image and textural information. To obtain recognizable characteristic details, we introduced a novel self-calibration module (SCM) between the 2 stages to reweight the local features, which realizes the complementation of information at different stages while refining feature information. In addition, we also designed an autoencoder neural network, using a self-supervised learning scheme to train a perceptual loss neural network specifically for CT images.
We evaluated the diagnostic quality of the results and performed ablation experiments on the loss function and network structure modules to verify each module's effectiveness in the network. Our proposed network architecture obtained high peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and visual information fidelity (VIF) values in terms of quantitative evaluation. In the analysis of qualitative results, our network structure maintained a better balance between eliminating image noise and preserving image details. Experimental results showed that our proposed network structure obtained better metrics and visual evaluation.
This study proposed a new LDCT image reconstruction method by combining autoencoder perceptual loss networks with multistage convolutional neural networks (MSCNN). Experimental results showed that the newly proposed method has performance than other methods.
计算机断层扫描（CT）因其能够无创检测人体内部结构而被广泛应用于医学诊断。然而，常规辐射剂量的CT扫描会对患者造成不可逆的损害。低剂量CT（LDCT）降低了辐射暴露，但CT图像中会出现大量斑点噪声和条纹伪影，甚至可能导致结构变形，严重影响其诊断能力。
本文提出了一种多阶段网络框架，将整个过程逐步划分为两个阶段的子网络来完成图像重建任务。具体而言，使用扩张残差卷积神经网络（DRCNN）对LDCT图像进行去噪。然后，将学习到的上下文信息与保留局部信息的通道注意力子网相结合，以保留图像的结构细节、特征和纹理信息。为了获得可识别的特征细节，我们在两个阶段之间引入了一个新颖的自校准模块（SCM）来重新加权局部特征，实现不同阶段信息的互补，同时细化特征信息。此外，我们还设计了一个自动编码器神经网络，采用自监督学习方案专门训练用于CT图像的感知损失神经网络。
我们评估了结果的诊断质量，并对损失函数和网络结构模块进行了消融实验，以验证每个模块在网络中的有效性。在定量评估方面，我们提出的网络架构获得了较高的峰值信噪比（PSNR）、结构相似性指数测量（SSIM）和视觉信息保真度（VIF）值。在定性结果分析中，我们的网络结构在消除图像噪声和保留图像细节之间保持了更好的平衡。实验结果表明，我们提出的网络结构获得了更好的指标和视觉评估。
本研究通过将自动编码器感知损失网络与多阶段卷积神经网络（MSCNN）相结合，提出了一种新的LDCT图像重建方法。实验结果表明，新提出的方法比其他方法具有更好的性能。
